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Commit 54c90764 authored by b1131ecdbc91571026ae3b991090f676's avatar b1131ecdbc91571026ae3b991090f676
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version de test

parent e131acd5
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Showing with 227 additions and 62 deletions
module2/exo5/exo5_fr.ipynb
View file @ 54c90764
......@@ -40,7 +40,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 12,
"metadata": {},
"outputs": [
{
......@@ -261,33 +261,33 @@
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
" Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 1,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
......@@ -322,7 +322,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 13,
"metadata": {},
"outputs": [
{
......@@ -355,14 +355,6 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
......@@ -387,6 +379,22 @@
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
......@@ -395,6 +403,54 @@
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/29/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
......@@ -403,6 +459,14 @@
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
......@@ -416,22 +480,31 @@
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"1 11/12/81 6 70 50 1\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 2,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = data[data.Malfunction>0]\n",
"#data = data[data.Malfunction>0]\n",
"data = data[data.Pressure==200]\n",
"data"
]
},
......@@ -448,12 +521,12 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -500,7 +573,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 15,
"metadata": {},
"outputs": [
{
......@@ -509,10 +582,10 @@
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 15</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 13</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
......@@ -521,16 +594,16 @@
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.0868</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231</td> \n",
" <th>Date:</th> <td>Wed, 03 Feb 2021</td> <th> Deviance: </th> <td> 1.9748</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>19:11:24</td> <th> Pearson chi2: </th> <td> 0.236</td> \n",
" <th>Time:</th> <td>13:14:12</td> <th> Pearson chi2: </th> <td> 2.84</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
......@@ -538,10 +611,10 @@
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n",
" <th>Intercept</th> <td> 4.2756</td> <td> 7.114</td> <td> 0.601</td> <td> 0.548</td> <td> -9.667</td> <td> 18.218</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n",
" <th>Temperature</th> <td> -0.0990</td> <td> 0.110</td> <td> -0.896</td> <td> 0.370</td> <td> -0.315</td> <td> 0.118</td>\n",
"</tr>\n",
"</table>"
],
......@@ -550,24 +623,109 @@
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n",
"Model: GLM Df Residuals: 5\n",
"Dep. Variable: Frequency No. Observations: 15\n",
"Model: GLM Df Residuals: 13\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5250\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n",
"Time: 19:11:24 Pearson chi2: 0.236\n",
"No. Iterations: 4 Covariance Type: nonrobust\n",
"Method: IRLS Log-Likelihood: -3.0868\n",
"Date: Wed, 03 Feb 2021 Deviance: 1.9748\n",
"Time: 13:14:12 Pearson chi2: 2.84\n",
"No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n",
"Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n",
"Intercept 4.2756 7.114 0.601 0.548 -9.667 18.218\n",
"Temperature -0.0990 0.110 -0.896 0.370 -0.315 0.118\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 4,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.api as sm\n",
"\n",
"data[\"Success\"]=data.Count-data.Malfunction\n",
"data[\"Intercept\"]=1\n",
"\n",
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.9210</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 03 Feb 2021</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>13:07:49</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Wed, 03 Feb 2021 Deviance: 3.0144\n",
"Time: 13:07:49 Pearson chi2: 5.00\n",
"No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n",
"Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
......@@ -605,12 +763,12 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -648,7 +806,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"outputs": [
{
......@@ -686,6 +844,13 @@
"analyse et de regarder ce jeu de données sous tous les angles afin\n",
"d'expliquer ce qui ne va pas."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
......@@ -705,7 +870,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.4"
}
},
"nbformat": 4,
......
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